Starting in 2005, across the world, a contagious fire was ignited in people’s brains. The cause of this: a user-friendly tech solution called Google Earth. With geospatial data as its engine, this innovation changed may peoples’ lives forever, from for Joe Public to Pam Property – to this author.
However, geospatial’s impact on property – a particularly data-heavy industry – was most significant.
Data is the oil that property runs on. Property players around the world are required to wrestle with the combination of maps, title deeds / land registries / registry of deeds / torrens system, and cadaster / surveyor general / bureau of land management, properties and tenants data.
This fresh geospatial angle made a lot of previously complex data simple.
It provided the missing golden thread that allowed Pam Property to tie the disparate data jewels above together.
This article unpacks early stage geospatial technology, empowers the reader with an understanding of geospatial data, and then moves out of the theory into four efficient, revenue generating and cost saving applications.
Google Earth – the stepping stone into the world of geospatial technology to many – allowed every day humans to see the earth from the sky, like a bird would. Easily and simply.
Further to this fresh, new perspective, Google Earth empowered property decision-makers and analysts (then and now) with wondrous tools like measurement devices. Pins could be customised with colours and shapes, even branded. Polygons could be made opaque, outlined and outlined. Files, that when clicked on, from anywhere, could navigate you exactly to that property on a map – like magic. This information could be stored neatly in folders. These collections of information could be emailed to colleagues, and viewed on their machines. Powerful for presentations, and effective to communicate unique insights and perspectives.
So, if geospatial technology is used to collect, analyse and store geographic data, what is geospatial data, and how does it apply to property?
For starters, commercial property is all about location. And location is the heartbeat of geospatial data. By way of explanation, geospatial data can be categorised into the following 3 Ps.
Fusing CRE with these 3Ps allows you to ask different data questions, and activates a whole new way of solving problems.
Further, geospatial provides a common language for CRE that transcends geographies, and sidesteps subjectivity. No matter your legal frameworks, a country’s language or the structure of an industry, any commercial property can be described by geospatial data. And geospatial data can talk to other geospatial data. Think of geospatial as an Esperanto for the world’s property players!
Let’s return to these 3 Ps, and unpack them further:
A point is the same as a pin on a map. It is defined by a combination of a latitude and longitude, known as co-ordinates.
The beautiful thing about geospatial data is that it all talks to each other. So as soon as your point is assigned to co-ordinates, it can talk to other geospatial data, and be enriched by that data.
For example, think about the point where you are reading this now. This point probably lives inside of a polygon-shaped suburb. That suburb, along with others, is nested inside of a larger city or town area. Which, in turn, lives within either a county, province or state. Which resides, again, in an even larger collection of counties, provinces or states, called a country.
So, from a single humble point (a latitude and longitude), it is possible, with razor sharp accuracy, in high speed, to enrich that point with a suburb, city, province, and country. In tech parlance – this point can inherit other attributes.
Thus, once you have a collection of these points, you can slice and dice your data by this, new, enriched information. Further, those points can be used in connection with the Ps that follow…
A collection of points – be they roads, public transport routes, or fibreoptic paths – are, in turn, interesting. Paths pass along or through points, and live inside of polygons. Paths can be assigned buffers on each side and these buffers can be turned into polygons. These buffers can communicate the catchment area of a bus route, or target customers for high speed fibre optics.
Paths can be either Euclidian (straight line or as the crow flies), or they can conform to how people move through urban or natural geographies. Path distances can be measured by travel time or distance.
Out of interest, the data harvesters for these travel times are humans, using Google Maps or Waze. This data, anonymised and aggregated, empowers Uber drivers to predict at what time they will arrive at to collect a passenger, and when that passenger will arrive at their destination. A path allows delivery vehicles to sequence delivery drop offs, ensuring prime cargo is delivered on time. Again, as with points, paths can inherit related point and polygon data
It’s perhaps easiest to think of a polygon as a net.
A simple suburb can be a polygon. So too, the floor plan of a property. So too, the land perimeter of said property. So too, all those locations that can be reached by car within 5 minutes of your house during rush hour. So too, collections of the closest possible points, relative to your competitors, that a consumer is likely to walk to, before that consumer walks to your competitor.
Why are polygons so useful in CRE? They allow you to objectively define geographies for comparison purposes – for example, vacancy rates within two polygons or equal or comparable size. This allows analysts to eliminate subjectivity when comparing different data sets.
Polygons are also used in other ways. In dense cities, where the boundaries between one neighbourhood and another are often a matter of opinion, polygons alleviate the need for debate: simply draw a polygon, as if throwing a net, over selected geo-located data points.
When data is assigned geospatial attributes (co-ordinates), no matter what form the data takes, it is beautifully structured for analysis.
Enough theory. Now the $29T question – how could this be used in the commercial real estate industry?
In 2017 fires raged through the Garden Route in South Africa, damaging beautiful holiday homes and ravaging businesses. An insurer client of ours, who had insured risks in those regions, was overwhelmed by a flurry of fire claims. The resulting admin was crippling, and the risk administration process intimidating and expensive to resolve. Fortunately that client had geocoded their properties. The fire range was known, and was defined by a polygon. With these two knowns, the way forward was beautifully elegant. By our client throwing this polygon at their claims data, it was possible to very quickly, without sending inspectors onsite, to call out spurious and opportunistic claims. And, unfortunately for the chancers, commence a separate process!
Could their sites house QSRs? If so, was there a list of these sites? What is the under-sweated value of all sites?
So how was geospatial technology and data used. From a listing of all sites, these sites were geocoded (i.e. turned into points). Travel distance polygons were thrown around these points, defining a catchment area, and this catchment area was thrown at census data to understand demographics. The site points were thrown at the land registry / surveyor / cadaster and deeds registries to identify the perimeters of the erven. By defining polygons describing onsite transport reticulation, existing structures and grades, retaining walls and servitudes, it was possible to identify strategic sites for standalone sites and redevelopment. So now we have QSR supply.
What about the demand? Next up was to do identify all quick services restaurants in the country, and travel distances from each quick service restaurant to the nearest site.
Picture this scenario: Retailer ABC is looking for standalone sites where they do not have an existing offering within X kms travel distance, where Z of the following Y competitors are in operation, and where the demographics satisfy ABC criteria.
At the conclusion of this exercise, these incredibly powerful answers could be delivered in mere button clicks, to boardrooms of slack-jawed decision-makers. Outcome: more retail space was identified than double that of one of the most well-known shopping centre in South Africa. And the most logical customers could be targeted on those sites.
Enter a large office node in early 2019… Whoever you spoke to at the time had vacancies ranging from 15% to 30%. Nobody knew for sure. So how do you answer this question conclusively?
Well, first you define the node.
How do you do that? Use a polygon. A polygon can give you a very precise description of the node – in certain instances suburbs making up that node are bisected, in others they are enclosed. Certain roads are fully encapsulated by the polygon, while others are only partially enclosed in the polygon net. Next throw your geolocated vacancies data at the net, and return results. To concerned property owners, eventually there is light, clarity and efficiency. Data is now firm, answers are auditable and eventually trustworthy, and better decisions can get made – more efficiently.
Now for a fibre operator. Large sums of money are being spent on laying down high quality, cheap, lightning fast fibre.
But how to efficiently introduce the superior product, at a lower price point product to customers? How do these businesses, hungry for cost savings, efficiently get to know of the great opportunity available to them?
Again, let’s look at what is known. The fibre routes are known. The buffer (area serviceable beyond a fibre route) is also known. So now you have a net of supply to throw at potential demand. But how to identify the customers who are needing this better priced, superior product. Well, if those customers are geospatially defined, you simply throw your net, and it will come back with gold.
Geospatial is a new age buzzword that may seem either intimidating or irrelevant – possibly both. However, in reading this, you may have realised two things:
Perhaps, while reading, you found your mind applying it to day to day life – where you live, the routes you travel on, every time you look at a map – digital or paper? Maybe, you started thinking about solving an existing problem in real estate a slightly different way? Or better yet, you experienced the green shoots of a business opportunity crystallising? Irrespective, this is the wonder and experience of geospatial – a simple problem-solving perspective which us human, in the whole, are deprived of, and the birds take for granted!
In short, geospatial is an accessible and intuitive paradigm for solving previously complex/unsolvable problems.
With the 3 Ps bedded down, there is a fourth P. It is our hope that the concrete examples above indicate how businesses are using geospatial to win in the fourth, and most important, P – profit.
In a long-overdue industry first, PropTech is now delivering professionally-designed, automated vacancy schedules to the CRE industry. You time-starved CRE professionals can now get your vacancy schedules to work as hard as you do, giving your vacant space the best chance of finding the right tenant.
Good news – CRE professionals can now seamlessly get their stock for sale or to let onto the listing platforms. With one click of a button. These data flows are now automated, making CRE more efficient. Say goodbye to double capture.
How the best CRE brokerages are applying the economic principle of opportunity cost to automate, outsource and focus. The result: higher profits, happier staff. We go from the theoretical to the practical, unpacking this in hard numbers.
